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With the increasing complexity of business processes in today's organizations and the ever-growing amount of structured accounting data, identifying erroneous or fraudulent business transactions and corresponding journal entries poses a major challenge for public accountants at annual audits. In current audit practice, mainly static rules are applied which check only a few attributes of a journal entry for suspicious values. Encouraged by numerous successful adoptions of deep learning in various domains we suggest an approach for applying autoencoder neural networks to detect unusual journal entries within individual financial accounts. The identified journal entries are compared to a list of entries that were manually tagged by two experienced auditors. The comparison shows high f-scores and high recall for all analyzed financial accounts. Additionally, the autoencoder identifies anomalous journal entries that have been overlooked by the auditors. The results underpin the applicability and usefulness of deep learning techniques in financial statement audits.

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The success and effectiveness of disaster management increasingly depend on the ability of a disaster response team to quickly and flexibly react to changes in unanticipated exceptional situations. Workflow management systems can be used to support the process organization of a disaster scenario. However, most workflow management systems are only applicable for domains with well structured and static processes. So, the highly dynamic characteristics of disaster management cannot be handled by current workflow management systems. In this paper, we present our approach of generic workflows that is based on the concept of generic functions and can be combined with the word field theory. We introduce the term mini story as a semantically logical unit for the construction of complex workflows. The elaborated concepts represent an essential part of a framework, which will be developed in the context of INDYCO project ("Integrated Dynamic Decision Support System Component for Disaster Management Systems").

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